Classification of Speech for Clinical Data using Artificial Neural Network
A wide range of researches are carried out in speech signal processing for denoising, enhancement and more. Besides the other, stress management is important to improve disabled children speech. In order to provide proper speech practice for the disabled children, their speech is analyzed. Initially, the normal and pathological subjects speech are obtained with the same set of words. In this paper, classification of normal and pathological subjects speech is discussed. Initially Feature Extraction is implemented using well known Mel Frequency Cepstrum Coefficients (MFCC) for both words of normal and pathological subjects speech. Dimensionality reduction of features extracted is implemented using Principal Component Analysis (PCA). Finally the features are trained using Artificial Neural Network (ANN) for classification.
Keywords: speech signal, stress management, Mel Frequency Cepstrum Coefficients (MFCC), Principal Component Analysis (PCA), trained
Download Full-Text
ABOUT THE AUTHORS
C.R.Bharathi
Research Scholar , Sathyabama University, Chennai. Assistant Professor,ECE Dept., Vel Tech University, Chennai. AMIE, ME(Appld. Elec.), M.Phil(Computer Science)
V. Shanthi
Professor,MCA Department, St. Joseph\'s college of Engg., Chennai. Co-author
C.R.Bharathi
Research Scholar , Sathyabama University, Chennai. Assistant Professor,ECE Dept., Vel Tech University, Chennai. AMIE, ME(Appld. Elec.), M.Phil(Computer Science)
V. Shanthi
Professor,MCA Department, St. Joseph\'s college of Engg., Chennai. Co-author
